title: Bioinformatics for RNomics.
abstract: Rapid improvements in high-throughput experimental technologies make it nowadays possible to study the expression, as well as changes in expression, of whole transcriptomes under different environmental conditions in a detailed view. We describe current approaches to identify genome-wide functional RNA transcripts (experimentally as well as computationally), and focus on computational methods that may be utilized to disclose their function. While genome databases offer a wealth of information about known and putative functions for protein-coding genes, functional information for novel non-coding RNA genes is almost nonexistent. This is mainly explained by the lack of established software tools to efficiently reveal the function and evolutionary origin of non-coding RNA genes. Here, we describe in detail computational approaches one may follow to annotate and classify an RNA transcript.
authors.affiliation: Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
authors.forename: Kristin
authors.initials: K
authors.lastname: Reiche
authors.forename: Katharina
authors.initials: K
authors.lastname: Schutt
authors.forename: Kerstin
authors.initials: K
authors.lastname: Boll
authors.forename: Friedemann
authors.initials: F
authors.lastname: Horn
authors.forename: Jörg
authors.initials: J
authors.lastname: Hackermüller
mesh_terms: D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D011506:Proteins / Q000235:genetics; D012313:RNA / Q000032:analysis* / Q000096:biosynthesis / Q000235:genetics; D012333:RNA, Messenger / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D022661:RNA, Untranslated / Q000032:analysis / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins; D012333:RNA, Messenger; D022661:RNA, Untranslated; D012313:RNA